How this analysis was created: This summary and feature list are AI-generated from collected project material and can contain mistakes. Stars, license and language are imported from GitHub. Inclusion does not mean that we have tested or audited this project. Check the source documentation for any feature you depend on. Learn more on our About page.
Wenliang Zhong 1 , Haoyu Tang 1 , Qinghai Zheng 2 , Mingzhu Xu 1 , Yupeng Hu 1 , Weili Guan 3
The main features of zhong0x29a/mct are: Gradient Trajectory Matching.
Projects with overlapping indexed features include: georgecazenavette/mtt-distillation — This repo contains code for training expert trajectories and distilling synthetic data from our Dataset Distillation… gzyaftermath/datm — Code. justincui03/tesla — Hello!!! Thanks for checking out our repo and paper! 🍻. nialiu/att — This repository contains code for training expert trajectories and distilling synthetic data for the paper: Dataset… nus-hpc-ai-lab/edf — In this work, we propose to emphasize discriminative features for dataset distillation in the complex scenario, i.e.… angusdujw/ftd-distillation — This repo contains code for training expert trajectories and distilling synthetic data from our Dataset Distillation…
This repo contains code for training expert trajectories and distilling synthetic data from our Dataset Distillation by Matching Training Trajectories paper (CVPR 2022). Please see our project page for more results.
This repo contains code for training expert trajectories and distilling synthetic data from our Dataset Distillation by FTD paper (CVPR 2023).
